Guides readers from the first steps of a deillegalscriptive analysis to the application of multivariate methods
Covers the most commonly used statistical methods in scientific applications
Includes a wealth of code snippets to obtain graphical and numerical representations of all results
Prof. K. Gerald van den Boogaart is the head of the Modeling and Valuation Department of the Helmholtz Institute Freiberg for Resource Technology and holds the chair of Applied Stochastic at TU Bergakademie Freiberg. Previously he was a professor of statistics in Greifswald. Throughout his career he has worked closely with various geoscientists, biologists and engineers.
Raimon Tolosana-Delgado is an Engineering Geologist and holds an MSc in Environmental Sciences. He received his PhD from the University of Girona, recognized as one of the world´s leading centers for Compositional Data Analysis. He has worked, mainly in the fields of sedimentology and oceanography, at several universities in Spain and Germany. He is currently a fellow researcher at the Helmholtz Institute Freiberg for Resource Technology.
This book presents the statistical analysis of compositional data sets, i.e., data in percentages, proportions, concentrations, etc. The subject is covered from its grounding principles to the practical use in deillegalscriptive exploratory analysis, robust linear models and advanced multivariate statistical methods, including zeros and missing values, and paying special attention to data visualization and model display issues. Many illustrated examples and code chunks guide the reader into their modeling and interpretation. And, though the book primarily serves as a reference guide for the R package "compositions,” it is also a general introductory text on Compositional Data Analysis.
Awareness of their special characteristics spread in the Geosciences in the early sixties, but a strategy for properly dealing with them was not available until the works of Aitchison in the eighties. Since then, research has expanded our understanding of their theoretical principles and the potentials and limitations of their interpretation. This is the first comprehensive textbook addressing these issues, as well as their practical implications with regard to software.
The book is intended for scientists interested in statistically analyzing their compositional data. The subject enjoys relatively broad awareness in the geosciences and environmental sciences, but the spectrum of recent applications also covers areas like medicine, official statistics, and economics.
Readers should be familiar with basic univariate and multivariate statistics. Knowledge of R is recommended but not required, as the book is self-contained.
Introduction.- Fundamental Concepts of Compositional Data Analysis.- Distributions for Random Compositions.- Deillegalscriptive Analysis of Compositional Data.- Linear Models for Compositions.- Multivariate Statistics.- Zeroes, Missings and Outliers.- References.- Index.
From the reviews:
"This book offers not only the theoretical background to analyse and interpret compositional data, but also the R support and guidance for the compositions package. The book is organised in 7 chapters. ... The book is built in an accessible manner for undergraduates and postgraduates alike and offers an all in one overview of the analysis of compositional data in R.” (Irina Ioana Mohorianu, zbMATH, Vol. 1276, 2014)
This book covers statistical analysis of compositional data sets from basic principles to applications in deillegalscriptive exploratory analysis, robust linear models and advanced multivariate statistical methods. Offers many illustrated examples and code chunks.
This book presents the statistical analysis of compositional data sets, i.e., data in percentages, proportions, concentrations, etc. The subject is covered from its grounding principles to the practical use in deillegalscriptive exploratory analysis, robust linear models and advanced multivariate statistical methods, including zeros and missing values, and paying special attention to data visualization and model display issues. Many illustrated examples and code chunks guide the reader into their modeling and interpretation. And, though the book primarily serves as a reference guide for the R package "compositions," it is also a general introductory text on Compositional Data Analysis.
Awareness of their special characteristics spread in the Geosciences in the early sixties, but a strategy for properly dealing with them was not available until the works of Aitchison in the eighties. Since then, research has expanded our understanding of their theoretical principles and the potentials and limitations of their interpretation. This is the first comprehensive textbook addressing these issues, as well as their practical implications with regard to software.
The book is intended for scientists interested in statistically analyzing their compositional data. The subject enjoys relatively broad awareness in the geosciences and environmental sciences, but the spectrum of recent applications also covers areas like medicine, official statistics, and economics.
Readers should be familiar with basic univariate and multivariate statistics. Knowledge of R is recommended but not required, as the book is self-contained.
From the reviews:
"This book offers not only the theoretical background to analyse and interpret compositional data, but also the R support and guidance for the compositions package. The book is organised in 7 chapters. ... The book is built in an accessible manner for undergraduates and postgraduates alike and offers an all in one overview of the analysis of compositional data in R." (Irina Ioana Mohorianu, zbMATH, Vol. 1276, 2014)
Prof. K. Gerald van den Boogaart is the head of the Modeling and Valuation Department of the Helmholtz Institute Freiberg for Resource Technology and holds the chair of Applied Stochastic at TU Bergakademie Freiberg. Previously he was a professor of statistics in Greifswald. Throughout his career he has worked closely with various geoscientists, biologists and engineers. Raimon Tolosana-Delgado is an Engineering Geologist and holds an MSc in Environmental Sciences. He received his PhD from the University of Girona, recognized as one of the world's leading centers for Compositional Data Analysis. He has worked, mainly in the fields of sedimentology and oceanography, at several universities in Spain and Germany. He is currently a fellow researcher at the Helmholtz Institute Freiberg for Resource Technology.
Über den Autor
Prof. K. Gerald van den Boogaart is the head of the Modeling and Valuation Department of the Helmholtz Institute Freiberg for Resource Technology and holds the chair of Applied Stochastic at TU Bergakademie Freiberg. Previously he was a professor of statistics in Greifswald. Throughout his career he has worked closely with various geoscientists, biologists and engineers.
Raimon Tolosana-Delgado is an Engineering Geologist and holds an MSc in Environmental Sciences. He received his PhD from the University of Girona, recognized as one of the world's leading centers for Compositional Data Analysis. He has worked, mainly in the fields of sedimentology and oceanography, at several universities in Spain and Germany. He is currently a fellow researcher at the Helmholtz Institute Freiberg for Resource Technology.
Inhaltsverzeichnis
Introduction.- Fundamental Concepts of Compositional Data Analysis.- Distributions for Random Compositions.- Deillegalscriptive Analysis of Compositional Data.- Linear Models for Compositions.- Multivariate Statistics.- Zeroes, Missings and Outliers.- References.- Index.
Klappentext
This book presents the statistical analysis of compositional data sets, i.e., data in percentages, proportions, concentrations, etc. The subject is covered from its grounding principles to the practical use in deillegalscriptive exploratory analysis, robust linear models and advanced multivariate statistical methods, including zeros and missing values, and paying special attention to data visualization and model display issues. Many illustrated examples and code chunks guide the reader into their modeling and interpretation. And, though the book primarily serves as a reference guide for the R package "compositions," it is also a general introductory text on Compositional Data Analysis.
Awareness of their special characteristics spread in the Geosciences in the early sixties, but a strategy for properly dealing with them was not available until the works of Aitchison in the eighties. Since then, research has expanded our understanding of their theoretical principles and the potentials and limitations of their interpretation. This is the first comprehensive textbook addressing these issues, as well as their practical implications with regard to software.
The book is intended for scientists interested in statistically analyzing their compositional data. The subject enjoys relatively broad awareness in the geosciences and environmental sciences, but the spectrum of recent applications also covers areas like medicine, official statistics, and economics.
Readers should be familiar with basic univariate and multivariate statistics. Knowledge of R is recommended but not required, as the book is self-contained.
Guides readers from the first steps of a deillegalscriptive analysis to the application of multivariate methods
Covers the most commonly used statistical methods in scientific applications
Includes a wealth of code snippets to obtain graphical and numerical representations of all results
Includes supplementary material: sn.pub/extras
